When authenticating an individual's identity via an individual's voice, a general objective is to decide, when given an identity claim (e.g. a name), whether the speech data of the user making the claim matches the voiceprint (data model) of the claimant (target) better than data models of the background population. To support this capability, the claimant must be enrolled in the system. Some possible applications for voice authentication, among others, are for verification purposes for gaining access to a locked door, access to an automatic teller machine, or generally for obviating the use of physical keys or passwords (though it should be noted that keys or passwords may still be used in conjunction with the methods described herein) or for enrolling a voice in a database in similar contexts. An example of conventional voice authentication is described in “Conversational Biometrics” (S. H. Maes, EUROSPEECH99).
Normally, speech data is collected by the data collection agent which performs the necessary data analysis and passes the resulting feature set to the modeling or testing agents depending on whether the desired operation is enrollment or verification. (See FIG. 1). However, previous efforts have generally failed to undertake voice-based authentication in a manner that provides the degree of accuracy and effectiveness often sought.
Thus, a need has been recognized in connection with providing an improved approach to such voice-based authentication.